This research article presents a computational work followed by experiments to elaborate the response of a multicracked beam structures excited by traversed mass. The cracks are located at different positions having different relative crack depths. A fourth-order Runge-Kutta computational method is used here for finding the deflection of the cracked beam. The responses of damaged cantilever beam and fixed-fixed beam are considered in this work. During the traversing of the mass across the damaged structure, the response of the cracked structures has been analysed. The effects of mass, speed, crack severities and crack locations on the response of the structure have also been inspected. Computational work has been carried out with different masses at various speeds for the moving mass. The results obtained from the computational work have been validated with that of experiments and are found to be in good agreement.
Damage detection in a structure using the vibration signature is a quiet smart method for condition monitoring of the structure. In this problem, the Recurrent Neural Networks (RNNs) based method has been implemented for damage detection in the moving load problem as an inverse method. A multi-cracked simply supported beam under a traversing load has been considered for the present problem. The localization and severities of the supervised cracks on the structure are determined using the adapted Jordan's Recurrent Neural Networks (JRNNs) approach. The mechanism of Levenberg-Marquardt's back propagation algorithm has been implemented to train the networks. To check the adoptability of the proposed JRNNs method, numerical analyses along with laboratory test verifications have been conducted and found to be well emerged.
The current work is based on the development of an inverse approach in the domain of Recurrent Neural Networks (RNNs) to identify and quantify cracks on a multi-cracked cantilever beam structure subjected to transit mass. At first, the responses of the multicracked structure subjected to transit load are determined using fourth order Runge-Kutta numerical method and finite element analysis (FEA) has been executed using ANSYS software to authenticate the employed numerical method. The existences and positions of cracks are identified from the measured dynamic excitation of the structure. The crack severities are found out by FEA as forward problem. The modified Elman's Recurrent Neural Networks (ERNNs) approach has been implemented as inverse problem to predict the locations and severities of cracks in the structure by applying Levenberg-Marquardt (LM) back propagation algorithm. The present analogy has been carried out in a supervised manner to check the convergence of the proposed algorithm. The proposed ERNNs method converge good results with those of theory and FEA.
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